Every time we create a machine learning model, we feed it with data to train it. Then we give the model some unlabeled data (test data) to check how well it performs and generalizes to new data. This model is stable if it works well on anonymous data, is consistent, and can forecast with high accuracy on a wide range of input data.
But, this isn't always the case! Machine learning models are not always stable; thus, we must assess their stability. Cross-Validation enters the scene at this point.